For CX and contact center leaders looking to turn customer conversations into strategic business advantage, the time to act is now.
Despite being rich in data, most contact centers remain poor in actionable insight.
While every customer conversation is recorded, few are truly analyzed, let alone used to inform enterprise-wide decision-making.
ContactBabel’s recent report, AI for Business Insights, outlines why this needs to change and how AI-enabled interaction analytics offers a path forward.
Why This Matters Now
Customer expectations are rising, budgets are tight and the pressure to do more with less has never been greater. Yet the majority of contact centers still rely on manual methods to analyze a small fraction of interactions. This approach leaves insights untapped, repeat contacts unresolved and valuable feedback siloed inside the contact center.
AI-enabled interaction analytics offers the capability to analyze 100% of customer interactions across voice and digital channels to uncover root causes, detect sentiment, identify failed journeys and surface competitive intelligence.
While every customer conversation is recorded, few are truly analyzed, let alone used to inform enterprise-wide decision-making.
Many contact centers that use AI-enabled analytics are doing so to improve first-contact resolution, with 42% stating that improving FCR and having fewer repeat calls is their primary KPI focus for analytics: this is wise, as FCR is a strong driver of customer experience.
AI analytics is increasingly used for the auto-categorization of calls, which is very useful as a starting point to investigate issues, processes or products which are driving repeat calls, as well as other negative outcomes such as complaints or customer churn.
Having automatic categorization take place at the end of the call means that agents not only spend less time on post-call work, but that any call disposition codes are more likely to be consistent and accurate: there are many large contact centers which offer more than 50 disposition codes to agents, few of whom will have the time or inclination to select the one most appropriate, always assuming in any case that every agent would judge the same call in the same way.
The following chart shows that insight from analytics is most widely used within the contact center itself, and is seen to generally be a useful way to improve KPIs and indeed CX.
It is interesting to note that this insight is used much less outside the contact center, with 25% of organizations using analytics simply not sharing this information with other business departments. Even in cases where insight is provided outside the contact center, as many respondents said that it was ineffective as said it was very effective.
This lack of cross-functional engagement represents a real missed opportunity to transform the contact center from a cost center to a strategic hub of customer intelligence.
This may be due to a number of reasons: interdepartmental communication and politics may play a part, as well as the non-contact center functions getting less involved in the initial process of deciding what to look for. This is currently a wasted opportunity for many organizations, and ways of gathering and sharing this insight across departments should be considered by senior leaders.
It seems that interaction analytics is gradually developing a positive following amongst those using it.
Analytics can also help organizations to identify which key performance indicators (KPIs) are actually most important to their business by correlating various performance and operational benchmarks against required business outcomes, such as understanding which operational KPIs and/or agent behaviors are linked with high levels of contract renewals or NPS scores.
Key Findings from the Report
- Understanding Contact Reasons
- Many contacts are avoidable. AI-enabled analytics reveals the causes of repeat calls and failed self-service, enabling upstream fixes and cost savings. Only 42% of U.S. contact centers report having a detailed understanding of failure demand, which is a significant blind spot.
- Sentiment Analysis Goes Deeper
- AI can now detect not just keywords, but also tone, volume and pace to assess emotional intensity. This allows for deeper understanding of what triggers customer frustration or delight, insights that can reshape product/service design and process improvements.
- Competitive and Product Intelligence
- Speech and text analytics help identify when competitors are mentioned or when products underperform. Businesses can act faster to tweak pricing strategies or fix friction points in the user journey, using real customer feedback.
- Real-Time Anomaly Detection
- Beyond dashboards, AI systems can alert teams in real time to emerging issues (e.g., website errors or billing glitches), helping prevent crises before they escalate.
- The Rise of Discovery and Root Cause Analytics
- Rather than relying on human hypothesis, discovery tools automatically surface patterns in data, pinpointing causes of dissatisfaction and service breakdowns without predefined search terms.
It seems that interaction analytics is gradually developing a positive following amongst those using it.
The following chart shows the proportion of survey respondents using analytics for the stated purpose who report that it is “very useful”, and it is interesting to note that survey respondents’ views of the usefulness of analytics for CX improvements have improved since 2018.
All of the use cases listed have seen a jump in the proportion of companies stating that they are ‘very useful’, with “identifying business process failures” rising from 40% in 2018 to 53% at the end of 2024, and the identification of self-service opportunities being particularly impressive.
Moving beyond predictive analytics, prescriptive analytics will help businesses understand how they are going to remedy a business problem based on the data that has already been collected and analyzed. This is where the adaptability of machine learning comes into play in terms of helping to map out a business’s next move, including simulating scenarios based on facts and probabilities rather than a business leader’s instinct.
With the right tools and organizational mindset, AI can help you not only hear what customers are saying, but truly understand what they feel and want from your organization.
The next level to analytics is unsupervised clustering powered by machine learning models that learn and uncover hidden patterns without human help, grouping words, questions and phrases with similar meanings. AI-powered anomaly models identify items, events or observations that do not conform to an expected pattern or other items in a dataset.
What Senior Leaders Should Consider Doing
- Reframe the Contact Center as a Strategic Asset
- AI analytics give the contact center a new role: a real-time barometer of business health and customer sentiment. This should elevate its importance in corporate strategy conversations.
- Break Down Silos Across Departments
- Insights must flow to product, marketing, IT and operations, not just stay in QA or CX. Consider forming cross-functional insight teams tasked with acting on analytics.
- Invest in Actionable Analytics
- Knowing what happened is not enough: understand not only the “why” but also what can be done to solve the issue. Seek platforms that offer root cause analysis, anomaly detection and prescriptive insights that directly inform next steps.
- Think Beyond Quality Assurance
- Most analytics investments start with compliance or performance monitoring. Expand the analytics use case to drive innovation, reduce churn and inform digital transformation.
- Make Sentiment and Emotion Data Work Harder
- Apply this data to refine coaching, target CSAT surveys, detect fraud and even boost sales conversions by modelling and sharing top-performer behavior.
Are You Listening or Just Hearing?
Customers are speaking to you all the time: are you just hearing them or actually listening?
With the right tools and organizational mindset, AI can help you not only hear what customers are saying, but truly understand what they feel and want from your organization.
It’s time to make every conversation count.
AI for Business Insights is available for download, free of charge from ContactBabel.